Computational Mental Imagery, and Visual Mechanisms for Maintaining a Goal-Subgoal Hierarchy

Mental imagery is one important and powerful mechanism of human cognition that has been little explored in AI research. I present a computational framework for mental imagery which specifies the representational and inferential primitives needed to form an imagery-based reasoning system. I then present a computational model of mental imagery that uses a novel recursive memory structure that I call Recursive Visual Memory (RVM), which provides a purely visual mechanism for maintaining a nested sequence of reasoning goals and subgoals. I tested this model on the same set of reasoning problems attempted by Evans’ classic ANALOGY program and show that, even without any prior geometric concepts such as shapes or lines, the use of RVM enables successful problem solving. I discuss several disciplines of AI that are commonly conflated with mental imagery and show why mental imagery is distinct. I close by considering broader impacts of this work, which include the construction of creative AI systems as well as improved understanding of individual differences in human cognition.